A general framework for unmet demand prediction in on-demand transport services

Wengen Li, Jiannong Cao, Jihong Guan, Shuigeng Zhou, Guanqing Liang, Winnie K.Y. So, Michal Szczecinski

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)


Emerging on-demand transport services, such as Uber and GoGoVan, usually face the dilemma of demand supply imbalance, meaning that the spatial distributions of orders and drivers are imbalanced. Due to such imbalance, much supply resource is wasted while a considerable amount of order demand cannot be met in time. To address this dilemma, knowing the unmet demand in the near future is of high importance for service providers because they can dispatch their vehicles in advance to alleviate the impending demand supply imbalance, we develop a general framework for predicting the unmet demand in future time slots. Under this framework, we first evaluate the predictability of unmet demand in on-demand transport services and find that unmet demand is highly predictable. Then, we extract both static and dynamic urban features relevant to unmet demand from data sets in multiple domains. Finally, multiple prediction models are trained to predict unmet demand by using the extracted features. As demonstrated via experiments, the proposed framework can predict unmet demand in on-demand transport services effectively and flexibly.

Original languageEnglish
Article number8500748
Pages (from-to)2820-2830
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number8
Publication statusPublished - Aug 2019


  • On-demand transport service
  • predictability
  • prediction model
  • unmet demand

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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